On May 9th, Vladimir Putin will oversee a parade in Moscow's Red Square, commemorating the Soviet Union's victory in the Second World War, an annual display of military bravado that, since Russia's full-scale invasion of Ukraine, in 2022, has taken on more explicit political undertones. The country's triumph over Nazism is presented as proof of its righteousness in the current war--and of it's role as a global power. Last year, as intercontinental ballistic missiles capable of carrying nuclear warheads rolled across the square, Putin linked the "radiant memory" of those who gave up their lives in the Second World War with "our brothers-in-arms who have fallen in the struggle against neo-Nazism and in the righteous fight for Russia"--that is, Russian soldiers killed in the current war in Ukraine. The Lede Reporting and commentary on what you need to know today. This year, the celebrations in Moscow serve another purpose: a way for Putin to show that he is not geopolitically isolated--China's Xi Jinping and Brazil's Luiz Inácio Lula da Silva are expected to attend.

Large language models (LLMs) have shown promise in providing scalable mental health support, while evaluating their counseling capability remains crucial to ensure both efficacy and safety. Existing evaluations are limited by the static assessment that focuses on knowledge tests, the single perspective that centers on user experience, and the open-loop framework that lacks actionable feedback. To address these issues, we propose Ψ-Arena, an interactive framework for comprehensive assessment and optimization of LLM-based counselors, featuring three key characteristics: (1) Realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients, (2) Tripartite evaluation that integrates assessments from the client, counselor, and supervisor perspectives, and (3) Closed-loop optimization that iteratively improves LLM counselors using diagnostic feedback. Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives. Moreover, reflection-based optimization results in up to a 141% improvement in counseling performance. We hope PsychoArena provides a foundational resource for advancing reliable and human-aligned LLM applications in mental healthcare.

Task-oriented semantic communication has emerged as a fundamental approach for enhancing performance in various communication scenarios. While recent advances in Generative Artificial Intelligence (GenAI), such as Large Language Models (LLMs), have been applied to semantic communication designs, the potential of Large Multimodal Models (LMMs) remains largely unexplored. In this paper, we investigate an LMM-based vehicle AI assistant using a Large Language and Vision Assistant (LLaVA) and propose a task-oriented semantic communication framework to facilitate efficient interaction between users and cloud servers. To reduce computational demands and shorten response time, we optimize LLaVA's image slicing to selectively focus on areas of utmost interest to users. Additionally, we assess the importance of image patches by combining objective and subjective user attention, adjusting energy usage for transmitting semantic information. This strategy optimizes resource utilization, ensuring precise transmission of critical information. We construct a Visual Question Answering (VQA) dataset for traffic scenarios to evaluate effectiveness. Experimental results show that our semantic communication framework significantly increases accuracy in answering questions under the same channel conditions, performing particularly well in environments with poor Signal-to-Noise Ratios (SNR). Accuracy can be improved by 13.4% at an SNR of 12dB and 33.1% at 10dB, respectively.

These are high-stakes conversations where clinicians must navigate weighty issues, where a poorly chosen word could have lasting consequences on a patient's final days and the memories their loved ones carry forward. Low-quality SIC has been associated with poor patient and family prognostic understanding [5], perceived lack of emotional support [6], lower quality healthcare outcomes and higher costs [7-13]. Communication with advanced-stage cancer patients specifically poses a variety of challenges, including: the volume and complexity of medical information, often fast-paced office visits, and the emotional burden of these life-changing conversations, for clinicians, patients, and their loved ones. Despite their extensive medical training, many physicians struggle to deliver difficult news effectively [14-16], often resulting in patient anxiety, misaligned treatment decisions, and reduced quality of care [17-19]. Also costly is the terms of expensive and potentially burdensome treatments as well as malpractice claims[20].

In the rapidly evolving educational landscape, the unbiased assessment of soft skills is a significant challenge, particularly in higher education. This paper presents a fuzzy logic approach that employs a Granular Linguistic Model of Phenomena integrated with multimodal analysis to evaluate soft skills in undergraduate students. By leveraging computational perceptions, this approach enables a structured breakdown of complex soft skill expressions, capturing nuanced behaviours with high granularity and addressing their inherent uncertainties, thereby enhancing interpretability and reliability. Experiments were conducted with undergraduate students using a developed tool that assesses soft skills such as decision-making, communication, and creativity. This tool identifies and quantifies subtle aspects of human interaction, such as facial expressions and gesture recognition. The findings reveal that the framework effectively consolidates multiple data inputs to produce meaningful and consistent assessments of soft skills, showing that integrating multiple modalities into the evaluation process significantly improves the quality of soft skills scores, making the assessment work transparent and understandable to educational stakeholders.

As AI enters the workforce and seeps into all facets of our lives at unprecedented speed, we're told by leaders across industries that if you're not using it, you're falling behind. Yet when AI's use in art enters the conversation, some retreat in discomfort, shunning it as an affront to the very essence of art. This ongoing debate continues to create disruptions among artists. AI is fundamentally changing the creative process, and its purpose, significance, and influence are subjective to one's own values--making its trajectory hard to predict, and even harder to confront. Miami-based Panamanian photographer Dahlia Dreszer stands out as an optimist and believer in AI's powers.

If you were asked to visualise a stereotypical British man, what would you think of? According to AI, the answer is an overweight man wearing a football shirt. Instagram account @reimagineuk asked AI to create videos of the most stereotypical men around the world - with hilarious results. While the British man looks casual in his football shirt, men from other countries are depicted with fancier outfits. The stereotypical man from Portugal sports a white shirt and a waistcoat, while the man from Nigeria can be seen wearing a bright orange suit.

The Heaviside activation function is for instance used in Hopfield networks [ 1 ] that have recently seen a resurge due to their connections t o attention layers [ 2, 3 ] and the 2024 Nobel Prize in Physics that was partially award ed for their development. Moreover, the Heaviside activation function is closely related to quantized neural networks [ 4, 5 ], playing a key role in enabling energy efficient deployment o f large language models (LLMs) [ 6, 7 ]. We refer to neural networks with several hidden layers and th e Heaviside activation function as deep Heaviside (neural) networks (DHNs). These networks are also known as (linear) threshold networks. The Heaviside activation function can be traced back to the fi rst attempts to build an artificial counterpart of a biological neuron. In the brain, the inputs of a neuron contribute to its membrane potential and the neuron discharges/fires if th e membrane potential exceeds a certain threshold.

This paper proposed an approach to automatically discovering subject dimension, action dimension, object dimension and adverbial dimension from texts to efficiently operate texts and support query in natural language. The high quality of trees guarantees that all subjects, actions, objects and adverbials and their subclass relations within texts can be represented. The independency of trees ensures that there is no redundant representation between trees. The expressiveness of trees ensures that the majority of sentences can be accessed from each tree and the rest of sentences can be accessed from at least one tree so that the tree-based search mechanism can support querying in natural language. Experiments show that the average precision, recall and F1-score of the abstraction trees constructed by the subclass relations of subject, action, object and adverbial are all greater than 80%. The application of the proposed approach to supporting query in natural language demonstrates that different types of question patterns for querying subject or object have high coverage of texts, and searching multiple trees on subject, action, object and adverbial according to the question pattern can quickly reduce search space to locate target sentences, which can support precise operation on texts.

Qualitative research often involves labor-intensive processes that are difficult to scale while preserving analytical depth. This paper introduces The AI Co-Ethnographer (AICoE), a novel end-to-end pipeline developed for qualitative research and designed to move beyond the limitations of simply automating code assignments, offering a more integrated approach. AICoE organizes the entire process, encompassing open coding, code consolidation, code application, and even pattern discovery, leading to a comprehensive analysis of qualitative data.